Autonomous exploration of unknown environments is a critical task in robotic search and rescue operations. Recently, hierarchical planning frameworks have gained significant attention for their potential to enhance exploration efficiency. However, most existing approaches struggle with efficient exploration due to two key limitations: (1) neglecting subregion environmental information and (2) inconsistency between local and global paths. To overcome these challenges, we propose an Information Entropy-assisted Hierarchical Planning (IEHP) framework for efficient autonomous exploration. Specifically, we introduce an efficient subregion arrangement method that considers total travel distance, path similarity, and information entropy. Additionally, we propose a globally consistent frontier selection method to minimize redundant local paths, improving alignment between local and global planning. We validate the feasibility and efficiency of our approach through a series of complex simulation scenarios, with experimental results demonstrating the superiority of the proposed method.

Point cloud registration suffers from repeated patterns and low geometric structures in indoor scenes. The recent transformer utilises attention mechanism to capture the global correlations in feature space and improves the registration performance. However, for indoor scenarios, global correlation loses its advantages as it cannot distinguish real useful features and noise. To address this problem, we propose an image-geometry-assisted point cloud registration method by integrating image information into point features and selectively fusing the geometric consistency with respect to reliable salient areas. Firstly, an Intra-Image-Geometry fusion module is proposed to integrate the texture and structure information into the point feature space by the cross-attention mechanism. Initial corresponding superpoints are acquired as salient anchors in the source and target. Then, a selective correlation fusion module is designed to embed the correlations between the salient anchors and points. During training, the saliency location and selective correlation fusion modules exchange information iteratively to identify the most reliable salient anchors and achieve effective feature fusion. The obtained distinctive point cloud features allow for accurate correspondence matching, leading to the success of indoor point cloud registration. Extensive experiments are conducted on 3DMatch and 3DLoMatch datasets to demonstrate the outstanding performance of the proposed approach compared to the state-of-the-art, particularly in those geometrically challenging cases such as repetitive patterns and low-geometry regions.

Most existing lifelong machine learning works focus on how to exploit previously accumulated experiences (e.g., knowledge library) from earlier tasks, and transfer it to learn a new task. However, when a lifelong learning system encounters a large pool of candidate tasks, the knowledge among various coming tasks are imbalance, and the system should intelligently choose the next one to learn. In this paper, an effective “human cognition” strategy is taken into consideration via actively sorting the importance of new tasks in the process of unknown-to-known, and preferentially selecting the most valuable task with more information to learn. To be specific, we assess the importance of each new coming task (e.g., unknown or not) as an outlier detection issue, and propose to employ a “watchdog” knowledge library to reconstruct each task under$\ell _0$-norm constraint. The coming candidate tasks are then sorted depending on the sparse reconstruction scores in a descending order, which is referred to as a “watchdog” mechanism. Following this, we design a hierarchical knowledge library for the lifelong learning framework to encode new task with higher reconstruction score, where the library consists of two-level task descriptors, i.e., a high-dimensional one with low-rank constraint and a low-dimensional one. Both “watchdog” knowledge library and hierarchy knowledge library can be optimized with knowledge from both previously learned tasks and current task automatically. For model optimization, we explore an alternating method to iteratively update our proposed framework with a guaranteed convergence. Experimental results on several existing benchmarks demonstrate that our proposed model outperforms various state-of-the-art task selection methods.

Underwater camera calibration has attracted much attentions due to its significance in high-precision three-dimensional (3-D) pose estimation and scene reconstruction. However, most existing calibration methods focus on calibrating the underwater camera in a single scenario [e.g., air-glass-water], which can not well formulate the geometry constraint and further result in the complex calibration process. Moreover, the calibration precision of these methods is low, since multilayer transparent refractions with unknown layer orientation and distance make the task more difficult than that in air. To address these challenges, we develop a novel and efficient medium-driven method for underwater camera calibration (MedUCC), which can calibrate the underwater camera parameters, including the orientation and position of the transparent glass accurately. Our key idea of this article is to leverage the light-path changes formed by medium refractions between different media to acquire calibration data, which can better formulate the geometry constraint, and estimate the initial value of the underwater camera parameters. To improve the calibration accuracy of the underwater camera system, a quaternion-based solution is developed to refine the underwater camera parameters. To the end, we evaluate the calibration performance on an underwater camera system. Extensive experiment results demonstrate that our proposed method can obtain a better performance in comparison to the existing works. We also validate our proposed MedUCC method on our designed 3-D scanner prototype, which illustrates the superiority of our proposed calibration method.

Most state-of-the-art underwater vision systems are calibrated manually in shallow water and used in open seas without changing. However, the refractivity of the water is adaptively changed depending on the salinity, temperature, depth or other underwater environmental indexes, which inevitably generate the calibration errors and induces incorrectness e.g., for underwater Simultaneously Localization and Mapping (SLAM). To address this issue, in this paper, we propose a new underwater Camera-Inertial Measurement Unit (IMU) calibration model, which just needs to be calibrated once in the air, and then both the intrinsic parameters and extrinsic parameters between the camera and IMU could be automatically calculated depending on the environment indexes. To our best knowledge, this is the first work to consider the underwater Camera-IMU calibration via environmental indexes. We also build a verification platform to validate the effectiveness of our proposed method on real experiments, and use it for underwater monocular Visual-Inertial SLAM.